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Robust hierarchical image representation using non-negative matrix factorisation with sparse code shrinkage preprocessing
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AI
DOI：10.1007/s10044-002-0185-3.png)
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摘要
En 中文
When analysing patterns, our goals are (i) to find structure in the presence of noise, (ii) to decompose the observed structure into sub-components, and (iii) to use the components for pattern completion. Here, a novel loop architecture is introduced to perform these tasks in an unsupervised manner. The architecture combines sparse code shrinkage with non-negative matrix factorisation, and blends their favourable properties: sparse code shrinkage aims to remove Gaussian noise in a robust fashion; non-negative matrix factorisation extracts substructures from the noise filtered inputs. The loop architecture performs robust pattern completion when organised into a two-layered hierarchy. We demonstrate the power of the proposed architecture on the so-called 'bar-problem' and on the FERET facial database.
Keyword:
hierarchy
non-negative matrix factorisation
sparse code shrinkage
